Abstract: In order to enhance the intelligence and unmanned operation level of the rapid quantitative loading system for coal, achieve real-time detection of train loading quality, and prevent occurrences of train overloading or misalignment, this study addresses the shortcomings of existing non-contact loading quality detection systems by proposing a coal rapid loading misalignment detection method based on laser radar point clouds. Integrating the loading process at coal train stations, the system utilizes laser radar three-dimensional scanning technology and car number recognition technology to establish a train loading quality detection system. A Mahalanobis distance-based outlier filtering algorithm is proposed, which conducts statistical analysis on the neighborhood of each point, calculating its Mahalanobis distance to nearby points. This process eliminates a significant amount of random noise present in the train car point clouds, such as dust during loading, sprayed water mist, and environmental disturbances (rain, snow, coal dust), etc. A label-connected domain clustering algorithm is introduced to segment between adjacent train cars through point cloud connected domain region labeling and clustering. Additionally, a train car stitching algorithm based on PCA analysis is proposed for the three-dimensional stitching of train car point clouds. A point cloud extraction method for loading materials based on point cloud slicing is presented, improving computational speed by constructing local point cloud neighborhoods. Slicing is performed in the length and width directions of the train car to filter out point clouds in front and behind, as well as on the left and right of the train car. Finally, calculation methods for key indicators of train loading quality, including loading height, coal loading quantity, and misalignment quantity, are proposed. The intuitive display of detection results is achieved through surface three-dimensional reconstruction, facilitating the quality inspection of train loading. Experimental results demonstrate that the proposed method enables real-time scanning modeling and loading quality detection on the surface of the train during loading, with applicability to different-sized vehicle models, indicating its generality.